Hierarchical representation for spatial knowledge

One of the fundamental issues in building autonomous agents is to be able to sense, represent and react to the world. Some of the earlier work [Mor83, Elf90, AyF89] has aimed towards a reconstructionist approach, where a number of sensors are used to obtain input that is used to construct a model of the world that mirrors the real world. Sensing and sensor fusion was thus an important aspect of such work. Such approaches have had limited success, and some of the main problems were the issues of uncertainty arising from sensor error and errors that accumulated in metric, quantitative models. Recent research has therefore looked at different ways of examining the problems. Instead of attempting to get the most accurate and correct model of the world, these approaches look at qualitative models to represent the world, which maintain relative and significant aspects of the environment rather than all aspects of the world. The relevant aspects of the world that are retained are determined by the task at hand which in turn determines how to sense. That is, task directed or purposive sensing is used to build a qualitative model of the world, which though inaccurate and incomplete is sufficient to solve the problem at hand. This paper examines the issues of building up a hierarchical knowledge representation of the environment with limited sensor input that can be actively acquired by an agent capable of interacting with the environment. Different tasks require different aspects of the environment to be abstracted out. For example, low level tasks such as navigation require aspects of the environment that are related to layout and obstacle placement. For the agent to be able to reposition itself in an environment, significant features of spatial situations and their relative placement need to be kept. For the agent to reason about objects in space, for example to determine the position of one object relative to another, the representation needs to retain information on relative locations of start and finish of the objects, that is endpoints of objects on a grid. For the agent to be able to do high level planning, the agent may need only the relative position of the starting point and destination, and not the low level details of endpoints, visual clues and so on. This indicates that a hierarchical approach would be suitable, such that each level in the hierarchy is at a different level of abstraction, and thus suitable for a different task. At the lowest level, the representation contains low level details of agent's motion and visual clues to allow the agent to navigate and reposition itself. At the next level of abstraction the aspects of the representation allow the agent to perform spatial reasoning, and finally the highest level of abstraction in the representation can be used by the agent for high level planning.